The agent assembled a clear explanation of momou.io by fetching the homepage, about page, and consulting services page directly from the site, supplemented by search results pointing to an AI-facts positioning page. The site was navigable but incomplete: core service and positioning content was accessible, but pricing remains opaque and case studies are absent (404 errors on work/projects pages), forcing the agent to note these gaps rather than fill them from memory.

• ›Steps [1], [4], [10], [11] directly fetched momou.io pages and returned substantive content on services, team philosophy, consulting offerings, and market positioning. These formed the backbone of the final answer without requiring model inference.
• ›Search results [9] surfaced the 'ai-facts-momou' page as a key differentiation resource; the agent then fetched it [11], yielding the most detailed positioning claims ('Swiss craft, zero buzzwords', 'product-driven', 'integrated team'). This was the only resource that explicitly addressed 'how they differ from alternatives.'
• ›Pricing is genuinely absent from the site: steps [1] and [5] (contact page) both report 'no pricing details.' The agent did not confabulate or apply model memory here; it correctly flagged this as a gap.
• ›Case study and detailed portfolio visibility failed: steps [6] and [7] hit 404s on /work and /projects URLs. The agent guessed these URLs from standard agency patterns (not surfaced by the site), so absence here reflects site navigation structure, not content retrieval failure.
• ›The agent's final 'confusing or missing' section [titled 'What Was Confusing or Missing'] is labeled as such but draws from model reasoning about what a user would expect from a digital agency, NOT from failed fetches or model memory about momou specifically. This is appropriate framing for a discovery task.